Minikanren enthusiasts: meet Medikanren https://www.uab.edu/mix/stories/a-high-speed-dr-house-for-medical-breakthroughs
@KitRedgrave maybe the ai winter was really just an ai cryogenic freeze
@cwebber i would love to know how they deal with ingesting all these papers and generating a suitable knowledge representation. are they using a different machine for that?
@KitRedgrave looks like it's all stored locally in a CSV. You can run it: https://github.com/webyrd/mediKanren
@KitRedgrave Oh, it's converting it I think somehow. I'm going to look at what it's using.
@KitRedgrave HAHA AWESOME
if I'm reading this write it loads it all into racket hashtables and then serializes them as racket files https://github.com/webyrd/mediKanren/blob/master/code/db.rkt
@KitRedgrave *if I'm reading this right
but oddly appropriate typo
@cwebber so it's a nifty use of minikanren but they have a ways to go
@notclacke @KitRedgrave Note that I didn't have the full story:
> There's a little more to it. Greg Rosenblatt created a nice, efficient graph database in Racket that is fast when reading data from disk. Works very well in practice! :)
@KitRedgrave @cwebber it looks like they use something called SemMedDB
@KitRedgrave @cwebber "The Semantic MEDLINE Database (SemMedDB) [1] is a repository of semantic predications (subject-predicate-object triples) extracted by SemRep, a semantic interpreter of biomedical text [2]."
@er1n Oh I didn't realize @KitRedgrave meant the data... I thought we were talking about the data store!
Not the only cool resource published by the us gov and in the public domain... the USDA nutrition database is also cool as hell https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/nutrient-data-laboratory/docs/sr28-download-files/
@cwebber @er1n yeah. reading that many papers and getting the knowledge is not at all trivial! interesting that they had some way of getting that large a knowledgebase... though one has to wonder how valid some of it is given that some branches of science have had bad problems with replicable results
@KitRedgrave @er1n I wonder if it's data the FDA itself used for its evaluation of whether or not a drug may be introduced to the market?
@KitRedgrave @er1n @cwebber No, as it hasn't been nearly validated enough. That might help guide what to study, but you are never going to beat a properly done three-phase trial of thousands of people for finding problems with a drug.
they hibernate periodically, waiting for strong enough computers to give them the ability to dominate the planet
though it is AI, so it'll probably get the killing patients bit right
@ninjawedding @KitRedgrave I thought the inclusion of House was pretty silly
> In 15 seconds, the program has an answer: Gleevec, a brand name for imatinib, was designed to treat cancer. But it also turns out to inhibit the BCR gene, which causes asthma. Why does mediKanren think so? The program serves up links to the relevant papers on PubMed, the NIH’s vast research database.
I love that this expert system even explains itself and offers supporting evidence. That's some next-level expert system stuff.
@cwebber this is highly interesting
@cwebber expert systems come back from the grave
or were they ever really dead